Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Bursa Technical University, Yildirim, Bursa, Turkey.
Department of Computer Engineering, Turkish Air Force Academy, National Defense University, Yesilyurt, Istanbul, Turkey.
Med Biol Eng Comput. 2019 Oct;57(10):2179-2201. doi: 10.1007/s11517-019-02024-8. Epub 2019 Aug 7.
It is of great importance to effectively process and interpret surface electromyogram (sEMG) signals to actuate a robotic and prosthetic exoskeleton hand needed by hand amputees. In this paper, we have proposed a cepstrum analysis-based method for classification of basic hand movement sEMG signals. Cepstral analysis technique primarily used for analyzing acoustic and seismological signals is effectively exploited to extract features of time-domain sEMG signals by computing mel-frequency cepstral coefficients (MFCCs). The extracted feature vector consisting of MFCCs is then forwarded to feed a generalized regression neural network (GRNN) so as to classify basic hand movements. The proposed method has been tested on sEMG for Basic Hand movements Data Set and achieved an average accuracy rate of 99.34% for the five individual subjects and an overall mean accuracy rate of 99.23% for the collective (mixed) dataset. The experimental results demonstrate that the proposed method surpasses most of the previous studies in point of classification accuracy. Discrimination ability of the cepstral features exploited in this study is quantified using Kruskal-Wallis statistical test. Evidenced by the experimental results, this study explores and establishes applicability and efficacy of cepstrum-based features in classifying sEMG signals of hand movements. Owing to the non-iterative training nature of the artificial neural network type adopted in the study, the proposed method does not demand much time to build up the model in the training phase. Graphical abstract.
对手部截肢患者而言，有效地处理和解释表面肌电 (sEMG) 信号对于控制机器人和假肢外骨骼手非常重要。在本文中，我们提出了一种基于倒谱分析的基本手部运动 sEMG 信号分类方法。倒谱分析技术主要用于分析声学和地震信号，通过计算梅尔频率倒谱系数 (MFCC)，有效地用于提取时域 sEMG 信号的特征。然后，将由 MFCC 组成的提取特征向量转发给广义回归神经网络 (GRNN) 进行分类。该方法已在基本手部运动数据集上进行了 sEMG 测试，对于五个个体受试者的平均准确率为 99.34%，对于混合数据集的总体平均准确率为 99.23%。实验结果表明，该方法在分类准确性方面超过了大多数先前的研究。本研究使用 Kruskal-Wallis 统计检验来量化所利用的倒谱特征的判别能力。实验结果表明，该研究探索并建立了基于倒谱的特征在手运动 sEMG 信号分类中的适用性和功效。由于研究中采用的人工神经网络类型具有非迭代训练性质，因此该方法在训练阶段不需要太多时间来建立模型。
